A Procedural Definition of Multi-word Lexical Units
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multi-word expressions evade a closed definition. Linguists and computational linguists rely on intuition or build lists of MWE types; while practical, that is scientifically and aesthetically unsatisfying. Without presuming to solve a daunting theoretical problem, we propose a decision procedure which steers a lexicographer toward acceptance or rejection of an N-gram as a lexical unit: a decision tree classifies N-grams as MWE or not MWE. It will succeed if it agrees with the native speakers’ judgment. We need a small, linguistically credible set of features, to contend with the multiplicity of adequate trees. Decision tree induction works with a fixed set of annotated classification examples, but the lexical material for MWE recognition is too large to make annotation feasible. We rely on small-scale statistically significant sampling, and on intuition. Of a few decision trees produced by informed trial and error, we select one we consider best in our circumstances. That tree, deployed in a large-scale wordnet construction project, allowed us to gather dependable statistics on its usefulness in lexicographers’ work. Our goal: systematic expansion of a wordnet by tens of thousands of MWEs in a manner as free of personal biases as possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it